Efficient shot change detection on compressed video data

ABSTRACT

A mask matching approach to detect shot changes in MPEG coded video uses reference ratio variances of macroblocks between MPEG coded frames. A function is designed to quantize the results into shot change probability values. Moreover, a conversion function modifies this probability computation to minimize misdetection and loss of detection under unusual image pattern situations. A shot change probability threshold is defined for the video to be examined. When a frame&#39;s modified shot change probability exceeds this threshold, the frame is considered to contain a shot change. With this approach, processing time is reduced by evaluating MPEG coded data directly, rather than the raw video.

FIELD OF THE INVENTION

The present invention relates to a method and apparatus for searchingvideo data. More specifically, the present invention relates to a methodand apparatus for detecting shot changes in compressed video data.

BACKGROUND OF THE INVENTION

The medium of digital video communication is widely used in manyapplications. Due to the rich information content of video data, queriescan be specified not only by video titles, video descriptions, andalpha-numeric attributes of video data, but also by the video contents.Therefore, video index construction for supporting powerful querycapabilities is an important research issue for video database systems.

Video segmentation is a fundamental step toward video indexconstruction. Video sequences may be segmented according to so-called"shot changes", which are often used for video browsing. A "shot" ismade up of a sequence of video frames which represents a continuousaction in time and space. Therefore, the contents of the framesbelonging to the same shot are similar. A shot change is defined as adiscontinuity between two shots. The similarity (or dissimilarity)measurement of continuous frames may therefore be used for shot changedetection.

In the prior art, many varied approaches have been explored in thedevelopment of indexing techniques. U.S. Pat. No. 5,212,547, dated May18, 1993, entitled "Image Processing Device and Method for SensingMoving Objects and Rangefinder Employing the Same", teaches findingobjects in motion in a frame. The video data of a frame is subtractedfrom the average value of the video data of the frame. This techniquedoes not involve shot change detection, however.

In U.S. Pat. No. 5,327,232, dated Jul. 5, 1994, entitled "Method andApparatus for Detecting Motion Vectors", the objective is to detect themotion vector of the content of a frame, using an image block matchingmethod. Again, this technique does not utilize shot change detection.

In U.S. Pat. No. 5,488,425, dated Jan. 30, 1996, entitled "Apparatus forStoring Video Information by Recognizing Video Frames", the objective isto select one frame and detect similar frames subsequent to it. Shotchange detection is not an objective of this invention.

In U.S. Pat. No. 5,179,449, dated Jan. 12, 1993, entitled "SceneBoundary Detecting Apparatus", the object to be processed is theoriginal video data, rather than the compressed video image frames. As aresult, the speed of processing is relatively slow.

In the paper entitled "A Feature-Based Algorithm for Detecting andClassifying Scene Breaks", by Ramin Zabih, et al. ACMMM95!, the subjectthesis detects an occurrence of a shot change by observing changes inthe positions of the lines in adjacent frames. Thus, the major featureof this method is the use of image analysis, whereby lines in a frameare detected for determination of a shot change. Since the data beingprocessed is the original video data, the speed of processing isrelatively slow.

In the paper entitled "Feature Management for Large Video Databases", byFarshid Arman, et al. SPIE93!, the subject thesis deals with DCT-basedcompressed video data where the DCT multiple parameters are used todetermine a shot change. In consecutive frames, an inner product isobtained through calculation of the DCT parameters of the block in thesame position. The greater the difference in the frames, the larger theinner product will be. This method is capable of determining a shotchange in a timely manner, since it does not analyze the original imagedata of the frame. However, when the inner product falls within a grayarea, so that it is difficult to determine whether or not there is ashot change, the frames must be decompressed and analyzed using originalimage data. Thus, the processing speed is compromised.

In the paper entitled "Projection Detecting Filter for Video CutDetection", by Kiyotaka Otsuji and Y. Tonomura ACMMM93!, the subjectthesis proposes a process of filtering, whereby the frame variationsthat are not caused by a shot change are reduced to a minimum, and thedetermination of the variations of a shot change in the frames issimplified. However, this thesis does not consider the use of compressedvideo data.

In the paper entitled "Knowledge Guided Parsing in Video Databases", byDeborah Swanberg et al. SPIE93!, the subject thesis proposes the use ofa color histogram difference method to determine a shot change. Thedistribution of the content of a frame is predicted according todifferent kinds of video data, so as to locate the position of a shotchange with accuracy, and to decide the classification of the shot atthe same time. The disadvantage of this method is that a knowledge baseof the video data must be defined on a case-by-case basis.

Therefore, it is an object of the present invention to overcome thedisadvantages of the prior art.

SUMMARY OF THE INVENTION

This and other objects are achieved by the present invention. Inaccordance with an illustrative embodiment of the present invention, asystem and method are provided for detecting shot changes in MPEG codedvideo. The system has a processor and a memory, as well as input andoutput devices. The processor illustratively executes the followingsteps in detecting a shot change:

(a) defining a shot change mask cluster based on the IBP-ratio of thecompressed MPEG coded video data,

(b) calculating a shot change probability function P for each frame ofthe MPEG video data, where each P and B frame is encoded with referencesto adjacent frames, and where the I frames are encoded independently,

(c) determining a threshold value T for the shot change probabilityfunction P,

(d) comparing the shot change probability function P for each frame withthe threshold value T, and

(e) detecting a shot change at any of the frames when that frame's shotchange probability function P exceeds the threshold value T.

The shot change mask cluster of step (a), above, is made up of asequence of I, P, and B types of frame masks, with low referencecharacteristics between adjacent frames within the frame mask. Theprocessor illustratively stores the mask cluster in the memory. Notethat the memory can also store the probability and threshold data andequations.

An illustrative embodiment of the present invention is more fullydescribed below in conjunction with the following drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a system according to an embodiment of the presentinvention.

FIG. 2 illustrates a typical MPEG frame sequence.

FIG. 3 illustrates an example video sequence.

FIG. 4 illustrates an example for computing shot change probability.

FIG. 5 is a flowchart which schematically illustrates a processaccording to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

According to one embodiment of the present invention, a system 100, asshown in FIG. 1, is used to analyze MPEG (e.g., ISO\IEC 11172-2 1993Information Technology--Coding of Moving Pictures and Associated Audiofor Digital Storage Media at up to about 1.5 Mbit/s--Part 2: Video or"MPEG-1" and ISO\IEC 13818-2 Information Technology--Generic Coding ofMoving Pictures and Associated Audio Information: Video, Nov. 11, 1994or "MPEG-2") compressed (encoded) video data. As shown, the apparatus100 has a processor 110, such as an Intel™ Pentium™ microprocessor, or aMotorola™ PowerPC 603™ microprocessor. The processor 110 executessuitable software for carrying out the functions described below. Theapparatus 100 also has a main memory 120 and a disk memory 130 forstoring the video database and queries executed thereon. The processor110, main memory 120, and disk memory 130 are connected to a bus 190,which transfers data, e.g., program instruction or video information,between the devices connected thereto. A display device 150, such as aliquid crystal display (LCD), or cathode ray tube (CRT) monitor, isprovided, which may be connected to the bus 190 via a graphics adapter(not shown). Illustratively, the display device 150 is capable ofdisplaying motion picture video thereon, for example, motion picturevideo of a database stored by the apparatus 100, or frames of a videoquery. A manual input device 160, such as a keyboard and mouse/pointingdevice, may also be provided, which is also connected to the bus 190.The manual input device can receive keypunch/pointing input from a user,for example, to generate queries as described below.

The MPEG standard for video compression is used in many applicationswhere a high level of video compression is desired. While the MPEGstandard applies to field pictures as well as to frame pictures, onlythe MPEG frame structure will be described herein in reference to theinventive shot change detection method. Because MPEG video is highlycompressed, shot change detection algorithms which perform on raw(uncompressed) video are not optimal for MPEG coded video, sinceadditional processing is required for decompressing the MPEG compressedvideo into raw video. Therefore, it is more efficient to detect shotchanges on MPEG compressed video directly.

The MPEG coding algorithm uses DCT (Discrete Cosine Transform) tocompress raw video data. Additionally, MPEG uses block-based motioncompensation to reduce temporal redundancy. By means of motioncompensation, codes of similar blocks can be reduced by referencing themto the image contents of adjacent frames. The more blocks a framereferences, the more similar the adjacent frames are. Therefore, byanalyzing the references among coded frames, their similarities (ordissimilarities) can be determined.

In the MPEG coding structure, a frame is divided into macroblocks. Eachmacroblock is a 16×16 image in the form of a basic coding unit. Amacroblock can be coded by DCT, or by references to its adjacent frameswhen it matches the similar image patterns of these adjacent frames. Amacroblock coded by DCT is called an intra-coded macroblock. Amacroblock which references to similar image patterns is called eitherforward-prediction coded, backward-prediction coded, orbidirectional-prediction coded, when it references to the image patternsof the preceding frame, subsequent frame, or both preceding andsubsequent frames, respectively. A reference to the preceding frame isnamed forward reference, and to the subsequent frame, backwardreference.

In accordance with the MPEG referencing patterns of macroblocks, thereare three types of frames; namely, I frame, P frame and B frame. Allmacroblocks in an I frame must be intra-coded. That is, the I frame isindependently coded, and can be decompressed without referencing toother frames. Macroblocks of the P frame may have forward references toits preceding I or P frame. That is, a P macroblock is aforward-prediction coded macroblock when a similar image pattern isfound in the preceding I or P frame. Otherwise, it is intra-coded when asimilar image pattern can not be found in the preceding I or P frame. AB frame may have references to its adjacent I or P frames. Themacroblock in a B frame can be a bidirectional-prediction coded,forward-prediction coded, or backward-prediction coded macroblock.

In MPEG coded video, the number and sequence of I, P, and B frames arepredetermined. In general, a number of P and B frames are situatedbetween two I frames, and a number of B frames may be between two Pframes, or between an I and a P frame. FIG. 2 illustrates a typicalstructure of MPEG coded frames. In FIG. 2, the ratio of the numbers ofI, P, and B frames (called the IPB-ratio) is 1:2:6. That is, an I frameis followed by two P frames and six B frames in the sequence shown.

For the P and B frames, macroblocks may reference to adjacent frames.The number of macroblocks for each type of reference may be computed asa reference ratio to measure the similarity between adjacent frames. Twotypes of reference ratios (RR's) are defined as follows:

    Forward reference ratio (FRR)=R.sub.f /N                   (1)

where R_(f) is the number of forward-prediction coded macroblocks in aframe, and N is the total number of macroblocks in the frame.

    Backward reference ratio (BRR)=R.sub.b /N,                 (2)

where R_(b) is the number of backward-prediction coded macroblocks in aframe, and N is the total number of macroblocks in the frame.

The range of an FRR or a BRR is between 0 and 1. A P frame may have anFRR. A B frame may have both an FRR and a BRR. When a P or B frame FRRis high, it indicates that the frame is similar to its preceding frame.When a P or B frame BRR is high, it indicates that the frame is similarto its subsequent frame. An RR is regarded as high when it exceeds agiven threshold value. An I frame has neither an FRR nor a BRR.Therefore, to measure the similarity between an I frame and its adjacentframes, the FRR's and BRR's of these adjacent frames must be evaluated.

In a video sequence, the contents of continuous frames are similar whenthere is no shot change. Therefore, the reference ratios of these framesare high. When a shot change occurs, however, the contents of subsequentframes are dissimilar to the preceding frames. Therefore, the referenceratios of these frames are low.

In the present invention, shot changes are detected by evaluating thereference ratios of MPEG coded frames. Therefore, only the informationregarding reference ratios has to be computed. This reduces processingtime, since there is no need to decompress each coded frame. Forexample, in a video sequence containing 10,000 continuous frames, eachframe is a 256×256 image. That is, a frame contains 256 macroblocks. Tocompute the reference ratio of a frame, only 256 add operations areneeded, as compared with 65536 (i.e., on the order of 10⁵) addoperations for color histogram or 10³ multiply operations for DCT-basedapproaches.

A shot change may occur in any type of frame. When a shot change occursat an I frame, the B frames between this I frame and the preceding I orP frames must be evaluated, since I frames are encoded independently ofother frames. The preceding B frames use this subsequent I frame as abackward reference for encoding. Because the preceding B frames are nowdissimilar to the image patterns of the subsequent I frame, the BRR's ofthe B frames must be low. The FRR's of these B frames are notconsidered, since they are not relevant to the subsequent I frame. The Bframes between this I frame and a subsequent P frame need not beconsidered either, since they are also not relevant to this shot changedetection method.

When a shot change occurs at a P frame, the B frames between this Pframe and a preceding I or P frame behave the same as in the previouscase of an I frame shot change. Unlike the I frame, however, the P framedoes have forward references. Since this P frame is the shot changeframe, its forward reference must be low, since it is now dissimilar tothe image patterns of the preceding I or P frames.

When a shot change occurs at a B frame, this B frame will have a lowFRR. If there are B frames between the shot change B frame and apreceding I or P frame, the BRR's of the preceding B frames must be low.If there are B frames between the shot change B frame and a subsequent Ior P frame, the FRR's of the subsequent B frames must be low. Also, ifthe first non-B frame following the shot change B frame is a P frame,the FRR of this subsequent P frame must be low.

To illustrate this evaluation technique, an MPEG video sequence with anIPB-ratio of 1:3:8 is shown in FIG. 3. If a shot change occurs at Iframe 13, for example, the B frames 11 and 12 will have low BRR's. If ashot change occurs at P frame 10, the BRR's of B frames 8 and 9 are low,as is the FRR of P frame 10. The situation is different, however, when ashot change occurs at B frame 5 as compared to B frame 6. If B frame 5is the shot change frame, P frame 7 and B frames 5 and 6 will have lowFRR's. If a shot change occurs at B frame 6, the BRR of B frame 5 willbe low, and the FRR's of P frame 7 and B frame 6 will be low as well.

Therefore, it is clear from the preceding analysis that a shot changecan be detected at a frame by examining and evaluating the FRR's and/orBRR's of that frame and its adjacent frames. To achieve this in thepresent invention, a mask matching approach is used to detect shotchanges by examining the MPEG coded video frame by frame. For each MPEGvideo film, a set of shot change masks (or mask cluster) is defined inaccordance with the IPB-ratio of the video sequence. Since there are I,P, and B frames, the types of masks are I₋₋ frame, P₋₋ frame, and B₋₋frame, respectively. The different types of frames must be matched withcorresponding types of masks. The RR's of the frames are then evaluatedand compared with the RR patterns specified in the corresponding shotchange masks. When a frame is matched with its associated mask, it isdetected as a shot change frame.

As defined herein, a shot change mask denotes the qualification fordetecting a shot change. The mask consists of two parts:

(1) the type of mask (I, P, or B)

(2) a sequence of mask frames

A mask frame M₁ can be denoted as follows:

M₁ =FR, where F .di-elect cons. {I, P, B}, and R .di-elect cons. {f, b}

F denotes the frame type (I or P or B), and R denotes the referenceratio (RR). When R=f, the forward reference ratio of the frame is low,while when R=b, the backward reference ratio of the frame is low. HighRR's are not used to detect the occurrences of shot changes.

A mask M can then be denoted as:

M={type; (M₁, M₂, . . . M_(n))},

where type .di-elect cons. {I, P, B}, and M_(i) are mask frames.

For example, if the IPB-ratio of an MPEG film is 1:2:6, its shot changemask cluster is configured as follows:

M₁ ={I; (Bb, Bb, @I)};

M₂ ={P; (Bb, Bb, @Pf)};

M₃ ={B; (@Bf, Bf, Pf) or (@Bf, Bf, I)};

M₄ ={B; (Bb, @Bf, Pf) or (Bb, @Bf, I)};

To denote the sequence of the frames, the mask frame beginning with an`@` indicates the current frame. Mask M₁ is for the I frame and mask M₂is for the P frame. Because of the IPB-ratio 1:2:6, the B frame may havetwo different situations; that is, a B frame may be preceded by an I ora P frame and followed by a B frame, or it may be preceded by a B frameand followed by an I or a P frame. Therefore, there are two masks, M₃and M₄, for the B frame. The M₃ mask indicates that the current B frameshould have a low FRR, its subsequent B frame should have a low FRR, andits subsequent P frame should have a low FRR. If the subsequent frame isan I frame, it can be ignored. Similarly, the M₄ mask indicates that thecurrent B frame should have a low FRR, its preceding B frame should havea low BRR, and its subsequent P frame should have a low FRR.

Referring again to FIG. 3, if I frame 13 is to be examined, the M₁ maskis applied. By checking the mask frames of M₁, the preceding two Bframes should have low BRRs when I frame 13 has a shot change. That is,B frame 11 and 12 have low BRR's.

In the present invention, the concept of mask matching depends on thedetermination of whether a frame has a low reference ratio. In order toestablish the meaning of "low", the reference ratio should be comparedwith a predefined threshold. Different types of videos may havedifferent thresholds.

To establish a threshold, the results of mask matching are firstquantized to a value which indicates the shot change probability. Theshot change probability function P is defined as follows: ##EQU1## wheref₁, f₂, . . . f_(n), .di-elect cons. the mask frames of the currentframe, and RR_(fi) is the corresponding reference ratio of mask framef_(i). If ∀ RR_(fi) =0, where 1≦i≦n, then P is set to 1.

The shot change probability ranges from 0 to 1. The larger the value,the more likely a shot change will occur at the frame being evaluated.The second term in Equation (3) is the weighted sum of the correspondingRR's of the mask frames. Thus, if one RR is much larger than the others,the result of the weighted sum will approach the larger RR. Thisemphasizes the effect of larger RR's on the probability function P.Therefore, the shot change probability will be low if there is a maskframe with a high RR.

For example, consider the video stream as shown in FIG. 4. The mask usedto detect P frame 6 is {P; (Bb, Bb, @Pf)}.

If the BRR of B frame 4, the BRR of B frame 5, and the FRR of P frame 6are all equal to 0.2, the probability that a shot change will occur at Pframe 6 is computed as (1-0.2)=0.8. This indicates that P frame 6 is ahighly probable shot change frame.

Referring again to FIG. 4, if the BRR of B frame 4 is 0.8, the BRR of Bframe 5 is 0.2 and the FRR of P frame 6 is 0.2. The shot changeprobability can be computed as (1-0.6)=0.4 by applying Equation (3). Inthis case, the probability of a shot change occurring at P frame 6 islow.

When all the shot change probabilities have been computed, a probabilitythreshold may be defined for implementing the inventive mask matchingmethod. That is, if the shot change probability of a frame is greaterthan the probability threshold, the frame is regarded as a shot changeframe.

An illustrative shot change probability threshold T is defined hereinas:

    T=(F+F')/2,                                                (4)

where F is the average probability of the 97.5% of all frames having thelowest probabilities, and F' is the average probability of the 2.5% ofall frames having the highest probabilities. The choice of 97.5% and2.5% is based on the assumption that there is approximately one shotchange for every 40 frames, on average. These two percentages may beadjusted for different types of videos. If both F and F' are less than0.5, the shot change probability threshold T is set at 0.5.

The above described mask matching method has been found to work verywell for most video applications. There are special situations, however,which may cause misdetections or loss of detections, as noted below:

(1) A sudden intensity change, as might be caused by an explosion,causes the frame similarities to be low. These problems are alsoencountered in other detection approaches.

(2) The contents between successive shots are very similar. Sincesimilarity measurement approaches compare frame image contents to detecta shot change, the highly similar contents, such as a series of verydark frames, may cause loss of detections. One way to reduce this effectis to dynamically adjust the probability threshold.

(3) A special IPB-ratio format, such as two consecutive I frames, with ashot change at the second I frame, will cause a loss of detection.

(4) A large object motion or sudden movement of the video camera, whichcauses the contents to change quickly may cause a misdetection to occur.

In order to reduce the number of misdetections caused by the specialsituations described above, a conversion function is used to adjust theshot change probabilities of Equation (3). This function is definedherein as follows: ##EQU2## The modified shot change probabilityfunction F(P_(i)) from Equation (5) reduces the effects of fast motionphenomena. Moreover, by adjusting the value of j, the problem of two ormore shot changes in a short period of time can be avoided.

The modified shot change probability function F(P_(i)) is used in thesame manner as described above for the shot change probability function(P). That is, if the modified shot change probability function F(P_(i))of a frame is greater than the probability threshold T, the frame isregarded as a shot change frame.

The inventive method disclosed herein is depicted in flow chart form inFIG. 5. Block 10 represents the compressed MPEG video being inputted tothe inventive system (illustratively, system 100 in FIG. 1) forexamination. In Block 20, the system detects the IPB-ratio of the MPEGinput, and forms the shot change mask cluster appropriate to this ratio.In Block 30, the shot change probability for each frame is calculated,according to Equation (3). Block 40 represents the shot changeprobability adjustment in accordance with Equation (5). In Block 50, theshot change probability threshold is computed in accordance withEquation (4). Finally, in Block 60, the frames are examined with respectto their associated masks. If a frame's modified shot change probabilityis greater than its shot change probability threshold value, the frameis considered to contain a shot change.

In short, an efficient mask matching method is disclosed whichautomatically detects shot changes in MPEG compressed video data.Moreover, the disclosed method reduces processing time by directlyevaluating MPEG coded data, rather than the original raw, ordecompressed video data. The improved efficiency provides greaterconvenience of data searching for the user, and enhances thefriendliness of an application system.

The above described embodiments of the invention are intended to beillustrative only. Numerous alternative embodiments may be devised bythose skilled in the art without departing from the spirit and scope ofthe following claims.

The invention claimed is:
 1. A method for detecting shot changes incompressed MPEG coded video data having I, P, and B frames, with an IPBratio corresponding to the numbers of I, P, and B frames, respectively,comprising the steps of:(a) defining a qualifying set of shot changemasks in accordance with said IPB ratio of said compressed MPEG codedvideo data, said qualifying set of shot change masks comprising asequence of I, P, and B types of frame masks, each of said I, P, and Btypes of frame masks having low reference ratio characteristics betweenadjacent frames within each I, P, and B type of frame mask, (b)calculating a shot change probability function P for each I, P, and Bframe of said video data, based on said reference ratio ofcharacteristics shot change frame masks, wherein each said P and B frameis encoded with references to adjacent frames, and wherein said I framesare encoded independently, (c) determining a threshold value T for saidshot change probability function P, (d) comparing said shot changeprobability function P for each said I, P, and B frame of said videodata with said threshold value T, and (e) detecting a shot change at anyof said I, P, and B frames of said video data when said shot changeprobability function P of said I, P, or B frame exceeds said thresholdvalue T.
 2. The method of claim 1 wherein step (b) calculates said shotchange probability function P for each said I, P, and B frame inaccordance with the following equation: ##EQU3## where f₁, f₂, . . .F_(n), .di-elect cons. said frame masks, and RR_(fi) is thecorresponding reference ratio of a frame mask f_(i).
 3. The method ofclaim 1 wherein step (c) determines said threshold value T for said shotchange probability function P in accordance with the following equation:##EQU4## where F is the average probability of 97.5% of all said frameshaving lowest probabilities, and F' is the average probability of 2.5%of all said frames having highest probabilities.
 4. The method of claim2 wherein step (b) further comprises an adjustment to said shot changeprobability function P for each said I, P, and B frame in accordancewith the following conversion function: ##EQU5## where i is the framenumber of said I, P, and B frame whose shot change probability iscurrently being computed,where j is a predetermined value whichrepresents a minimum distance between two successive shot changes, andwhere k is determined by said values of i and j.
 5. A method fordetecting shot changes in compressed MPEG coded video data having I, P,and B frames, with an IPB ratio corresponding to the numbers of I, P,and B frames, respectively, comprising the steps of:(a) defining a shotchange mask cluster in accordance with said IPB ratio of said compressedMPEG coded video data, wherein said shot change mask cluster comprises asequence of I, P, and B types of frame masks, each of said I, P, and Btypes of frame masks having low reference ratio characteristics betweenadjacent frames within each I, P, and B type of frame mask, (b)comparing said shot change mask cluster with each said I, P, and B frameof video data to find a match between said I, P, and B frame and itscorresponding type of frame mask within said shot change mask cluster,and (c) detecting a shot change at any of said I, P, and B frames ofsaid video data when one of said shot change mask cluster frame masksmatches one of a corresponding type of said I, P, and B frames.
 6. Asystem for detecting shot changes in compressed MPEG coded video datahaving I, P, and B frames, with an IPB ratio corresponding to thenumbers of I, P, and B frames, respectively, comprising:(a) an inputdevice for receiving MPEG coded data, (b) a processor for operating onsaid received data, (c) a memory for storing data operated on by saidprocessor, (d) an output device for displaying results from saidprocessor, (e) said processor for defining a shot change mask clusterbased on said IPB ratio of said compressed MPEG coded video data, (f)said processor for calculating a shot change probability function P foreach frame of said video data, wherein each said P and B frame isencoded with references to adjacent frames, and wherein said I framesare encoded independently, (g) said processor for determining athreshold value T for said shot change probability function P, (h) saidprocessor for comparing said shot change probability function P for eachsaid frame with said threshold value T, and (i) said processor fordetecting a shot change at any of said frames when said shot changeprobability function P of said frame exceeds said threshold value T,wherein said shot change mask cluster comprises a sequence of I, P, andB types of frame masks, each of said I, P, and B types of frame maskshaving low reference ratio characteristics between adjacent frameswithin said I, P, and B types of frame masks.
 7. The system of claim 6wherein step (b) calculates said shot change probability function P foreach I, P, and B frame in accordance with the following equation:##EQU6## where f₁, f₂, . . . f_(n), .di-elect cons. said frame masks,and RR_(fi) is the corresponding reference ratio of a frame mask f_(i).8. The system of claim 6 wherein step (c) determines said thresholdvalue T for said shot change probability function P in accordance withthe following equation: ##EQU7## where F is the average probability of97.5% of all said frames having lowest probabilities, and F' is theaverage probability of 2.5% of all said frames having highestprobabilities.
 9. The system of claim 7 wherein step (b) furthercomprises an adjustment to said shot change probability function P foreach said I, P, and B frame in accordance with the following conversionfunction: ##EQU8## where i is the frame number of said I, P, and B framewhose shot change probability is currently being computed,where j is apredetermined value which represents a minimum distance between twosuccessive shot changes, and where k is determined by said values of iand j.
 10. A system for detecting shot changes in compressed MPEG codedvideo data having I, P, and B frames, with an IPB ratio corresponding tothe numbers of I, P, and B frames, respectively, comprising:(a) aprocessor for defining a shot change mask cluster based on said IPBratio of said compressed MPEG coded video data, wherein said shot changemask cluster comprises a sequence of I, P, and B types of frame masks,each of said I, P, and B types of frame masks having low reference ratiocharacteristics between adjacent frames within said types of I, P, and Bframe masks, (b) said processor for comparing said shot change maskcluster with each said I, P, and B frame to find a match between said I,P, and B frame and its corresponding type of frame mask within said shotchange mask cluster, and (c) said processor for detecting a shot changeat any of said I, P, and B frames when one of said shot change maskcluster frame masks matches one of a corresponding type of said I, P,and B frames.